WGCNA GO Analysis
Kristen Brown, modified ZD
1/29/2024
WGCNA GO Analysis
sessionInfo() #provides list of loaded packages and version of R. ## R version 4.3.2 (2023-10-31)
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## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
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## Gu, Z. circlize implements and enhances circular visualization
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## If you use it in published research, please cite:
## Gu, Z. simplifyEnrichment: an R/Bioconductor package for Clustering and
## Visualizing Functional Enrichment Results, Genomics, Proteomics &
## Bioinformatics 2022.
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## ========================================Load in data
library(rtracklayer)
gff<-rtracklayer::import("../../data/Pocillopora_acuta_HIv2.genes_fixed.gff3")
gff<-as.data.frame(gff) %>% dplyr::select(-Parent)
dim(gff) # 478988 9## [1] 478988 12names(gff) ## [1] "seqnames" "start" "end" "width"
## [5] "strand" "source" "type" "score"
## [9] "phase" "ID" "transcript_id" "gene_id"transcripts <- subset(gff, type == "transcript")
transcripts_gr <- makeGRangesFromDataFrame(transcripts, keep.extra.columns=TRUE) #extract length information
transcript_lengths <- width(transcripts_gr) #isolate length of each gene
seqnames<-transcripts_gr$ID #extract list of gene id
lengths<-cbind(seqnames, transcript_lengths)
lengths<-as.data.frame(lengths) #convert to data frame
dim(transcripts) #33730 13## [1] 33730 12kegg <- read.delim("../../data/Pocillopora_acuta_HIv2.genes.KEGG_results.txt",header = FALSE)
kegg <- as.data.frame(kegg)
colnames(kegg)[1] <- "gene_id"
colnames(kegg)[2] <- "KEGG_new"
head(kegg)## gene_id KEGG_new
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a
## 2 Pocillopora_acuta_HIv2___RNAseq.g24143.t1b K22584
## 3 Pocillopora_acuta_HIv2___RNAseq.g22918.t1
## 4 Pocillopora_acuta_HIv2___RNAseq.g18333.t1 K03386
## 5 Pocillopora_acuta_HIv2___RNAseq.g7985.t1
## 6 Pocillopora_acuta_HIv2___RNAseq.g13511.t1eggnog<-read.delim("../../data/Pocillopora_acuta_HIv2.genes.EggNog_results.txt")#this file contains all of the go terms, descriptions, kegg, etc
eggnog<- plyr::rename(eggnog, c("X.query"="gene_id"))
head(eggnog,2)## gene_id seed_ortholog evalue score
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a 45351.EDO48725 2.16e-120 364
## 2 Pocillopora_acuta_HIv2___RNAseq.g18333.t1 45351.EDO38694 3.18e-123 355
## eggNOG_OGs
## 1 COG0620@1|root,KOG2263@2759|Eukaryota,38GZS@33154|Opisthokonta,3BNKS@33208|Metazoa
## 2 COG0450@1|root,KOG0852@2759|Eukaryota,38B9P@33154|Opisthokonta,3BGS4@33208|Metazoa
## max_annot_lvl COG_category
## 1 33208|Metazoa E
## 2 33208|Metazoa O
## Description Preferred_name
## 1 Cobalamin-independent synthase, Catalytic domain -
## 2 negative regulation of male germ cell proliferation PRDX4
## GOs
## 1 -
## 2 GO:0000003,GO:0001775,GO:0002252,GO:0002263,GO:0002274,GO:0002275,GO:0002283,GO:0002366,GO:0002376,GO:0002443,GO:0002444,GO:0002446,GO:0003006,GO:0003674,GO:0003824,GO:0004601,GO:0005488,GO:0005515,GO:0005575,GO:0005576,GO:0005615,GO:0005622,GO:0005623,GO:0005737,GO:0005783,GO:0005790,GO:0005829,GO:0006082,GO:0006457,GO:0006464,GO:0006468,GO:0006520,GO:0006575,GO:0006793,GO:0006796,GO:0006807,GO:0006810,GO:0006887,GO:0006915,GO:0006950,GO:0006952,GO:0006955,GO:0006979,GO:0007154,GO:0007165,GO:0007249,GO:0007252,GO:0007275,GO:0007276,GO:0007283,GO:0007548,GO:0008150,GO:0008152,GO:0008219,GO:0008285,GO:0008379,GO:0008406,GO:0008584,GO:0009056,GO:0009266,GO:0009409,GO:0009605,GO:0009607,GO:0009617,GO:0009628,GO:0009636,GO:0009893,GO:0009966,GO:0009967,GO:0009987,GO:0010467,GO:0010604,GO:0010646,GO:0010647,GO:0010941,GO:0010942,GO:0010950,GO:0010952,GO:0012501,GO:0012505,GO:0016043,GO:0016192,GO:0016209,GO:0016310,GO:0016491,GO:0016684,GO:0016999,GO:0017001,GO:0017144,GO:0019222,GO:0019471,GO:0019538,GO:0019725,GO:0019752,GO:0019953,GO:0022414,GO:0022417,GO:0023051,GO:0023052,GO:0023056,GO:0030141,GO:0030162,GO:0030198,GO:0031323,GO:0031325,GO:0031410,GO:0031974,GO:0031982,GO:0031983,GO:0032268,GO:0032270,GO:0032501,GO:0032502,GO:0032504,GO:0032940,GO:0033554,GO:0034774,GO:0035556,GO:0036211,GO:0036230,GO:0042119,GO:0042127,GO:0042221,GO:0042592,GO:0042737,GO:0042742,GO:0042743,GO:0042744,GO:0042802,GO:0042803,GO:0042981,GO:0043062,GO:0043065,GO:0043067,GO:0043068,GO:0043085,GO:0043170,GO:0043207,GO:0043226,GO:0043227,GO:0043229,GO:0043231,GO:0043233,GO:0043280,GO:0043281,GO:0043299,GO:0043312,GO:0043412,GO:0043436,GO:0043900,GO:0043901,GO:0044093,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044267,GO:0044281,GO:0044421,GO:0044422,GO:0044424,GO:0044433,GO:0044444,GO:0044446,GO:0044464,GO:0044703,GO:0045055,GO:0045137,GO:0045321,GO:0045454,GO:0045862,GO:0046425,GO:0046427,GO:0046546,GO:0046661,GO:0046903,GO:0046983,GO:0048232,GO:0048513,GO:0048518,GO:0048519,GO:0048522,GO:0048523,GO:0048583,GO:0048584,GO:0048608,GO:0048609,GO:0048731,GO:0048856,GO:0050789,GO:0050790,GO:0050794,GO:0050896,GO:0051171,GO:0051173,GO:0051179,GO:0051186,GO:0051187,GO:0051234,GO:0051239,GO:0051241,GO:0051246,GO:0051247,GO:0051336,GO:0051345,GO:0051604,GO:0051704,GO:0051707,GO:0051716,GO:0051920,GO:0052547,GO:0052548,GO:0055114,GO:0060205,GO:0060255,GO:0061458,GO:0065007,GO:0065008,GO:0065009,GO:0070013,GO:0070417,GO:0070887,GO:0071704,GO:0071840,GO:0072593,GO:0080090,GO:0097190,GO:0097237,GO:0097708,GO:0098542,GO:0098754,GO:0098869,GO:0099503,GO:0101002,GO:1901564,GO:1901605,GO:1902531,GO:1902533,GO:1904813,GO:1904892,GO:1904894,GO:1905936,GO:1905937,GO:1990748,GO:2000116,GO:2000241,GO:2000242,GO:2000254,GO:2000255,GO:2001056,GO:2001233,GO:2001235,GO:2001267,GO:2001269
## EC KEGG_ko
## 1 2.1.1.14 ko:K00549
## 2 1.11.1.15 ko:K03386
## KEGG_Pathway
## 1 ko00270,ko00450,ko01100,ko01110,ko01230,map00270,map00450,map01100,map01110,map01230
## 2 ko04214,map04214
## KEGG_Module KEGG_Reaction KEGG_rclass
## 1 M00017 R04405,R09365 RC00035,RC00113,RC01241
## 2 - - -
## BRITE KEGG_TC CAZy BiGG_Reaction
## 1 ko00000,ko00001,ko00002,ko01000 - - -
## 2 ko00000,ko00001,ko01000,ko04147 - - -
## PFAMs
## 1 Meth_synt_2
## 2 1-cysPrx_C,AhpC-TSAgogene <- merge(transcripts, eggnog, by=c("gene_id"), all=T)
gogene <- merge(gogene, kegg, by=c("gene_id"), all=T)
head(gogene,2)## gene_id seqnames
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t Pocillopora_acuta_HIv2___Sc0000013
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t Pocillopora_acuta_HIv2___Sc0000013
## start end width strand source type score.x phase
## 1 4542087 4551503 9417 + AUGUSTUS transcript NA NA
## 2 4639103 4647350 8248 + AUGUSTUS transcript NA NA
## ID
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t
## transcript_id seed_ortholog evalue score.y
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t 45351.EDO27354 2.41e-93 317
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t 6087.XP_002166004.2 1.28e-38 164
## eggNOG_OGs
## 1 COG0666@1|root,KOG0510@2759|Eukaryota,38G7Q@33154|Opisthokonta,3BCDU@33208|Metazoa
## 2 COG0666@1|root,KOG4177@2759|Eukaryota
## max_annot_lvl COG_category Description
## 1 33208|Metazoa DZ osmolarity-sensing cation channel activity
## 2 2759|Eukaryota I spectrin binding
## Preferred_name
## 1 TRPA1
## 2 -
## GOs
## 1 GO:0000302,GO:0001580,GO:0002791,GO:0002793,GO:0003008,GO:0003012,GO:0003674,GO:0004888,GO:0005034,GO:0005215,GO:0005216,GO:0005217,GO:0005244,GO:0005245,GO:0005261,GO:0005262,GO:0005488,GO:0005515,GO:0005575,GO:0005623,GO:0005886,GO:0005887,GO:0006810,GO:0006811,GO:0006812,GO:0006816,GO:0006873,GO:0006874,GO:0006875,GO:0006936,GO:0006939,GO:0006950,GO:0006979,GO:0007154,GO:0007165,GO:0007166,GO:0007204,GO:0007600,GO:0007602,GO:0007606,GO:0007610,GO:0007638,GO:0008150,GO:0008324,GO:0009266,GO:0009314,GO:0009408,GO:0009409,GO:0009410,GO:0009416,GO:0009453,GO:0009581,GO:0009582,GO:0009583,GO:0009593,GO:0009605,GO:0009612,GO:0009628,GO:0009636,GO:0009719,GO:0009966,GO:0009967,GO:0009987,GO:0010033,GO:0010035,GO:0010037,GO:0010243,GO:0010378,GO:0010646,GO:0010647,GO:0010817,GO:0014070,GO:0014074,GO:0014832,GO:0014848,GO:0015075,GO:0015085,GO:0015267,GO:0015276,GO:0015278,GO:0015318,GO:0016020,GO:0016021,GO:0016043,GO:0016048,GO:0016324,GO:0019233,GO:0019722,GO:0019725,GO:0019932,GO:0022607,GO:0022803,GO:0022832,GO:0022834,GO:0022836,GO:0022838,GO:0022839,GO:0022843,GO:0022857,GO:0022890,GO:0023041,GO:0023051,GO:0023052,GO:0023056,GO:0030001,GO:0030003,GO:0030424,GO:0031000,GO:0031224,GO:0031226,GO:0031644,GO:0031646,GO:0032024,GO:0032421,GO:0032501,GO:0032879,GO:0032880,GO:0032991,GO:0033554,GO:0033555,GO:0034220,GO:0034605,GO:0034702,GO:0034703,GO:0035556,GO:0035690,GO:0035774,GO:0036270,GO:0038023,GO:0040011,GO:0040040,GO:0042221,GO:0042330,GO:0042331,GO:0042391,GO:0042493,GO:0042542,GO:0042592,GO:0042752,GO:0042802,GO:0042995,GO:0043005,GO:0043052,GO:0043269,GO:0043270,GO:0043279,GO:0043933,GO:0044057,GO:0044070,GO:0044085,GO:0044425,GO:0044459,GO:0044464,GO:0045177,GO:0046677,GO:0046873,GO:0046883,GO:0046887,GO:0046957,GO:0048265,GO:0048518,GO:0048519,GO:0048522,GO:0048523,GO:0048583,GO:0048584,GO:0048878,GO:0050708,GO:0050714,GO:0050789,GO:0050794,GO:0050796,GO:0050801,GO:0050848,GO:0050850,GO:0050877,GO:0050896,GO:0050906,GO:0050907,GO:0050909,GO:0050912,GO:0050913,GO:0050951,GO:0050954,GO:0050955,GO:0050960,GO:0050961,GO:0050965,GO:0050966,GO:0050968,GO:0050974,GO:0050982,GO:0051046,GO:0051047,GO:0051049,GO:0051050,GO:0051179,GO:0051209,GO:0051222,GO:0051223,GO:0051234,GO:0051239,GO:0051240,GO:0051259,GO:0051260,GO:0051262,GO:0051282,GO:0051283,GO:0051289,GO:0051480,GO:0051606,GO:0051641,GO:0051649,GO:0051716,GO:0051930,GO:0051931,GO:0051969,GO:0052129,GO:0055065,GO:0055074,GO:0055080,GO:0055082,GO:0055085,GO:0060089,GO:0060341,GO:0060401,GO:0060402,GO:0061178,GO:0065003,GO:0065007,GO:0065008,GO:0070201,GO:0070417,GO:0070588,GO:0070838,GO:0070887,GO:0071241,GO:0071244,GO:0071310,GO:0071312,GO:0071313,GO:0071407,GO:0071415,GO:0071417,GO:0071466,GO:0071495,GO:0071840,GO:0071944,GO:0072347,GO:0072503,GO:0072507,GO:0072511,GO:0090087,GO:0090276,GO:0090277,GO:0097458,GO:0097553,GO:0097603,GO:0097604,GO:0098590,GO:0098655,GO:0098660,GO:0098662,GO:0098771,GO:0098796,GO:0098862,GO:0098900,GO:0098908,GO:0099094,GO:0099604,GO:0120025,GO:1901698,GO:1901699,GO:1901700,GO:1901701,GO:1902495,GO:1902531,GO:1902533,GO:1903522,GO:1903530,GO:1903532,GO:1903793,GO:1904058,GO:1904951,GO:1990351,GO:1990760
## 2 -
## EC KEGG_ko KEGG_Pathway KEGG_Module KEGG_Reaction KEGG_rclass
## 1 - ko:K04984 ko04750,map04750 - - -
## 2 - - - - - -
## BRITE KEGG_TC CAZy
## 1 ko00000,ko00001,ko04040 1.A.4.6.1,1.A.4.6.2,1.A.4.6.3,1.A.4.6.5 -
## 2 - - -
## BiGG_Reaction PFAMs KEGG_new
## 1 - Ank,Ank_2,Ank_3,Ank_4,Ion_trans K04984
## 2 - Ank,Ank_2,Ank_4,Ank_5,Ion_trans K04984dim(gogene)## [1] 33730 33geneInfo <- read.csv("../../output/WGCNA/WGCNA_ModuleMembership.csv") #this file was generated from the WGCNA analyses and contains the modules of interest
geneInfo<- plyr::rename(geneInfo, c("X"="gene_id"))
dim(geneInfo) # there are 9012 genes in our gene info file## [1] 9012 40geneInfo <- merge(gogene, geneInfo, by=c("gene_id")) #merging the GO and Kegg info to module membership for the 9012 genesFormat GO terms to remove dashes and quotes and separate by semicolons (replace , with ;) in GOs column
geneInfo$GOs <- gsub(",", ";", geneInfo$GOs)
geneInfo$GOs <- gsub('"', "", geneInfo$GOs)
geneInfo$GOs <- gsub("-", NA, geneInfo$GOs)
geneInfo$KEGG_new[geneInfo$KEGG_new == ""] <- NAunique(geneInfo$moduleColor)## [1] "green" "blue" "salmon" "turquoise" "yellow"
## [6] "black" "red" "magenta" "lightcyan" "purple"
## [11] "brown" "pink" "midnightblue" "tan" "cyan"geneInfo$Length<-lengths$transcript_lengths[match(geneInfo$gene_id, lengths$seqnames)]
dim(geneInfo)## [1] 9012 73write.csv(geneInfo, file = "../../output/WGCNA/GO_analysis/geneInfo_WGCNA.csv") #gene info for reference/supplementRun the GOSeq function by module color to test for GO term
enrichment. Due to high number of enriched GO terms, I am using a
p-value threshold of <0.05 and using rrvgo package to
reduce redundancy in list of GO terms.
Calcification - Up
### Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)### Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)calc_up_mods <- c("brown", "red", "black", "pink", "salmon", "blue")
nrow(geneInfo %>% dplyr::filter(moduleColor=="brown")) #942## [1] 942nrow(geneInfo %>% dplyr::filter(moduleColor=="red")) #425## [1] 425nrow(geneInfo %>% filter(moduleColor=="black")) #396## [1] 396nrow(geneInfo %>% filter(moduleColor=="pink")) #220## [1] 220nrow(geneInfo %>% filter(moduleColor=="salmon")) #154## [1] 154nrow(geneInfo %>% filter(moduleColor=="blue")) #1989## [1] 1989sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="brown")), nrow(geneInfo %>% dplyr::filter(moduleColor=="red")), nrow(geneInfo %>% filter(moduleColor=="black")), nrow(geneInfo %>% filter(moduleColor=="pink")), nrow(geneInfo %>% filter(moduleColor=="salmon")), nrow(geneInfo %>% filter(moduleColor=="blue")))## [1] 4126# 4126
calc_down_mods <- c("turquoise","magenta","lightcyan")
nrow(geneInfo %>% dplyr::filter(moduleColor=="turquoise")) #2558## [1] 2558nrow(geneInfo %>% dplyr::filter(moduleColor=="magenta")) #219## [1] 219nrow(geneInfo %>% filter(moduleColor=="lightcyan")) #65## [1] 65sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="turquoise")), nrow(geneInfo %>% dplyr::filter(moduleColor=="magenta")), nrow(geneInfo %>% filter(moduleColor=="lightcyan")))## [1] 2842# 2842
other_mods <- c("green","yellow", "purple", "midnightblue","cyan","tan")
sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="green")), nrow(geneInfo %>% dplyr::filter(moduleColor=="yellow")), nrow(geneInfo %>% filter(moduleColor=="purple")), nrow(geneInfo %>% filter(moduleColor=="midnightblue")), nrow(geneInfo %>% filter(moduleColor=="cyan")),nrow(geneInfo %>% filter(moduleColor=="tan")))## [1] 2044# 2044
# 4126 + 2842 + 2044 = 9012, which represents all of our genes4126 genes are in the 6 modules significantly upregulated by calcification.
### Generate vector with names in just the module we are analyzing
# ID.vector <- geneInfo %>%
# filter(moduleColor==c("brown", "red", "black", "pink", "salmon", "green")) %>%
# #get_rows(.data[[module]]))%>%
# pull(gene_id)
ID.vector <- geneInfo %>%
filter(moduleColor %in% c("brown", "red", "black", "pink", "salmon", "blue")) %>%
pull(gene_id)
length(ID.vector) #4126## [1] 4126##Get a list of GO Terms for the all 9012 genes
GO.terms <- geneInfo %>%
dplyr::select(gene_id, GOs) %>% rename(GOs = "GO.terms")
dim(GO.terms) #9012 2## [1] 9012 2##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms$gene, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("gene", "GO.terms")
GO.terms<-split2##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector)
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) ## Warning in pcls(G): initial point very close to some inequality constraints
#run goseq using Wallenius method for all categories of GO terms
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columnsGO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"#Filtering for p < 0.05
GO_05 <- GO %>%
dplyr::filter(over_represented_pvalue<0.05) %>%
dplyr::arrange(., ontology, over_represented_pvalue)#Write file of results
write.csv(GO_05, file = "../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv")GO_05 <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv")go_results_BP <- GO_05 %>%
filter(ontology=="BP")%>%
filter(over_represented_pvalue != "NA") %>%
#filter(numInCat>10)%>%
arrange(., over_represented_pvalue)
dim(go_results_BP)## [1] 654 8head(go_results_BP)## X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0050794 8.436918e-08 1.0000000 1395
## 2 2 GO:0065007 1.553493e-06 0.9999989 1574
## 3 3 GO:0050789 1.584882e-06 0.9999989 1479
## 4 4 GO:0048523 1.581326e-05 0.9999878 800
## 5 5 GO:0048518 3.043369e-05 0.9999758 950
## 6 6 GO:0051336 3.403563e-05 0.9999783 232
## numInCat term ontology
## 1 2785 regulation of cellular process BP
## 2 3184 biological regulation BP
## 3 2981 regulation of biological process BP
## 4 1569 negative regulation of cellular process BP
## 5 1888 positive regulation of biological process BP
## 6 415 regulation of hydrolase activity BPCollapsing with simplifyEnrichment package
library(simplifyEnrichment)
BP_terms <- go_results_BP$GOterm
Mat <- GO_similarity(BP_terms, ont = "BP", db = "org.Ce.eg.db")
#compare_clustering_methods(Mat, plot_type = "heatmap")
pdf(file = "../../output/WGCNA/GO_analysis/calc_up_mods_GOSEM.pdf", width = 7, height = 5)
simplifyGO(Mat, word_cloud_grob_param = list(max_width = 50), max_words=20)## Cluster 654 terms by 'binary_cut'... 184 clusters, used 2.05056 secs.
## 'magick' package is suggested to install to give better rasterization.
##
## Set `ht_opt$message = FALSE` to turn off this message.
## Perform keywords enrichment for 6 GO lists...dev.off()## quartz_off_screen
## 2CalcUpMods_GO_P05 <- go_results_BP %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Upregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcUpMods_GO_P05
ggsave("../../output/WGCNA/GO_analysis/CalcUpMods_GO_P05.pdf", CalcUpMods_GO_P05, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results_BP$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")## preparing gene to GO mapping data...## preparing IC data... #calculate similarity
scores <- setNames(-log(go_results_BP$over_represented_pvalue), go_results_BP$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
dim(reducedTerms)## [1] 508 10#keep only the goterms from the reduced list
go_results_BP_reduced <- go_results_BP %>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results_BP_reduced$ParentTerm <- reducedTerms$parentTerm[match(go_results_BP_reduced$GOterm, reducedTerms$go)]
length(unique(go_results_BP_reduced$ParentTerm))## [1] 73The reduced list of terms is 508 terms that falls under 73 parent terms.
write.csv(go_results_BP_reduced, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")Plot reduced terms
#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot_calcification <- ggplot(go_results_BP_reduced, aes(x = ontology, y = term)) +
geom_point(aes(size=over_represented_pvalue)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot_calcification
ggsave(filename="../../output/WGCNA/GO_analysis/GO.plot_calcification.pdf", plot=GO.plot_calcification, dpi=300, height=100, units="in", limitsize=FALSE)## Saving 7 x 100 in imageCalcUpMods_GO_P05_reduced <- go_results_BP_reduced %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Upregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcUpMods_GO_P05_reduced
ggsave("../../output/WGCNA/GO_analysis/CalcUpMods_GO_P05_reduced.pdf", CalcUpMods_GO_P05_reduced, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)Calcification - Down
2842 genes are in the 4 modules significantly downregulated by calcification.
### Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)### Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)### Generate vector with names in just the module we are analyzing
ID.vector <- geneInfo%>%
filter(moduleColor %in% c("turquoise","magenta","lightcyan"))%>%
pull(gene_id)
length(ID.vector) #2842## [1] 2842##Get a list of GO Terms for the all 9012 genes
GO.terms <- geneInfo %>%
dplyr::select(gene_id, GOs) %>% rename(GOs = "GO.terms")
dim(GO.terms) #9012 2## [1] 9012 2##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms$gene, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("gene", "GO.terms")
GO.terms<-split2##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector)
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) 
#run goseq using Wallenius method for all categories of GO terms
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columnsGO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"#Filtering for p < 0.05
GO_05 <- GO %>%
dplyr::filter(over_represented_pvalue<0.05) %>%
dplyr::arrange(., ontology, over_represented_pvalue)#Write file of results
write.csv(GO_05, file = "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv")GO_05 <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv")go_results_BP <- GO_05 %>%
filter(ontology=="BP") %>%
filter(over_represented_pvalue != "NA") %>%
#filter(numInCat > 10) %>%
arrange(., over_represented_pvalue)
dim(go_results_BP)## [1] 298 8head(go_results_BP)## X GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0002181 8.295294e-15 1 67
## 2 2 GO:0006614 2.463649e-13 1 48
## 3 3 GO:0006412 3.096109e-13 1 123
## 4 4 GO:0043043 1.598313e-12 1 126
## 5 5 GO:0006613 2.273026e-12 1 49
## 6 6 GO:0006518 7.543937e-12 1 146
## numInCat term ontology
## 1 90 cytoplasmic translation BP
## 2 59 SRP-dependent cotranslational protein targeting to membrane BP
## 3 219 translation BP
## 4 231 peptide biosynthetic process BP
## 5 63 cotranslational protein targeting to membrane BP
## 6 285 peptide metabolic process BPCollapsing with simplifyEnrichment package
library(simplifyEnrichment)
BP_terms <- go_results_BP$GOterm
Mat <- GO_similarity(BP_terms, ont = "BP", db = "org.Ce.eg.db")
pdf(file = "../../output/WGCNA/GO_analysis/calc_down_mods_GOSEM.pdf", width = 7, height = 5)
simplifyGO(Mat, word_cloud_grob_param = list(max_width = 50), max_words=20)## Cluster 298 terms by 'binary_cut'... 87 clusters, used 0.521641 secs.
## 'magick' package is suggested to install to give better rasterization.
##
## Set `ht_opt$message = FALSE` to turn off this message.
## Perform keywords enrichment for 4 GO lists...dev.off()## quartz_off_screen
## 2CalcDownMods_GO_P05 <- go_results_BP %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Downregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcDownMods_GO_P05
ggsave("../../output/WGCNA/GO_analysis/CalcDownMods_GO_P05.pdf", CalcDownMods_GO_P05, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results_BP$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")## preparing gene to GO mapping data...## preparing IC data... #calculate similarity
scores <- setNames(-log(go_results_BP$over_represented_pvalue), go_results_BP$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
dim(reducedTerms)## [1] 226 10#keep only the goterms from the reduced list
go_results_BP_reduced <- go_results_BP %>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results_BP_reduced$ParentTerm <- reducedTerms$parentTerm[match(go_results_BP_reduced$GOterm, reducedTerms$go)]
length(unique(go_results_BP_reduced$ParentTerm))## [1] 38The reduced list of terms is 226 terms that falls under 38 parent terms.
write.csv(go_results_BP_reduced, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot_calcification_down <- ggplot(go_results_BP_reduced, aes(x = ontology, y = term)) +
geom_point(aes(size=over_represented_pvalue)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot_calcification_down
ggsave(filename="../../output/WGCNA/GO_analysis/GO.plot_calcification_down.pdf", plot=GO.plot_calcification_down, dpi=300, height=100, units="in", limitsize=FALSE)## Saving 7 x 100 in imageCalcDownMods_GO_P05_reduced <- go_results_BP_reduced %>%
mutate(term = fct_reorder(term, over_represented_pvalue, .desc = TRUE)) %>%
ggplot(aes(x=term, y=over_represented_pvalue) ) +
geom_segment(aes(x=term ,xend=term, y=0, yend=over_represented_pvalue), color="grey") +
geom_point(size=1, color="#69b3a2") +
coord_flip() +
theme(
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_blank(),
legend.position="none"
) +
xlab("") +
ylab("over_represented_pvalue") +
ggtitle("Biological Process GO Terms Enriched in Modules Downregulated in Calcification") + #add a main title
theme(plot.title = element_text(face = 'bold',
size = 3,
hjust = 0)) +
theme_bw() + #Set background color
theme(panel.border = element_blank(), # Set border
panel.grid.major = element_blank(), #Set major gridlines
panel.grid.minor = element_blank(), #Set minor gridlines
axis.line = element_line(colour = "black"), #Set axes color
axis.text.y = element_text(size = 2), #set y axis labels size
plot.background=element_blank(),#Set the plot background
legend.position="none")
CalcDownMods_GO_P05_reduced
ggsave("../../output/WGCNA/GO_analysis/CalcDownMods_GO_P05_reduced.pdf", CalcDownMods_GO_P05_reduced, width = 12, height = 36, units = c("in"), dpi=300, limitsize=FALSE)code for by module
library(rrvgo)
# Define the unique module colors
module_colors <- na.omit(unique(geneInfo$moduleColor))
# Generate vector with names of all genes
ALL.vector <- c(geneInfo$gene_id)
# Generate length vector for all genes
LENGTH.vector <- as.integer(geneInfo$Length)
# Loop over each unique module color
for (color in module_colors) {
# Filter geneInfo based on the current color
color_filtered <- geneInfo %>% filter(moduleColor == color)
# Generate vector with names in just the module we are analyzing
ID.vector <- color_filtered$gene_id
length(ID.vector)
# Get a list of GO Terms for each module
GO.terms <- color_filtered %>%
dplyr::select(GOs, gene_id) %>%
rename(GOs = "GO.terms")
dim(GO.terms)
## Format to have one GO term per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";")
split2 <- data.frame(v1 = rep.int(GO.terms$gene_id, sapply(split, length)), v2 = unlist(split))
colnames(split2) <- c("gene", "GO.terms")
GO.terms <- split2
## Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector <- as.integer(ALL.vector %in% ID.vector)
names(gene.vector) <- ALL.vector # set names
# Weight gene vector by bias for length of gene
pwf <- nullp(gene.vector, ID.vector, bias.data = LENGTH.vector)
# Run goseq using Wallenius method for all categories of GO terms
GO.wall <- goseq(pwf, ID.vector, gene2cat = GO.terms, test.cats = c("GO:BP", "GO:MF", "GO:CC"), method = "Wallenius", use_genes_without_cat = TRUE)
GO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"
# Adjust p-values
GO$bh_adjust <- p.adjust(GO$over_represented_pvalue, method = "BH")
# Filtering for p < 0.01
GO <- GO %>%
dplyr::filter(bh_adjust < 0.00001) %>%
dplyr::arrange(., ontology, bh_adjust)
# Write file of results
write.csv(GO, file = paste0("../../output/WGCNA/GO_analysis/goseq_pattern_", color, ".csv"))
go_results <- GO
go_results<-go_results%>%
filter(ontology=="BP")%>%
filter(bh_adjust != "NA") %>%
#filter(numInCat>100)%>%
arrange(., bh_adjust)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(go_results$GOterm,
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")
#calculate similarity
scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
scores,
threshold=0.7,
orgdb="org.Ce.eg.db")
#keep only the goterms from the reduced list
go_results <- go_results %>% filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]
#plot significantly enriched GO terms by Slim Category faceted by slim term
GO.plot <- ggplot(go_results, aes(x = ontology, y = term)) +
geom_point(aes(size=bh_adjust)) +
scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
strip.text.y = element_text(angle=0, size = 10),
strip.text.x = element_text(size = 20),
axis.text = element_text(size = 8),
axis.title.x = element_blank(),
axis.title.y = element_blank())
GO.plot
length(colnames(go_results)[go_results$ParentTerm=="cation transport"])
length(colnames(go_results)[go_results$ParentTerm=="inorganic ion homeostasis"])
length(colnames(go_results)[go_results$ParentTerm=="regulation of cellular response to stress"])
}Merge the frequency of GOterms for both up- and down-regulation of calcification
Reduce the lists together
GO_05_up <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv") %>% dplyr::select(-"X")
GO_05_down <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv") %>% dplyr::select(-"X")go_results_BP_up <- GO_05_up %>%
filter(ontology=="BP")%>%
filter(over_represented_pvalue != "NA") %>%
#filter(numInCat>10)%>%
arrange(., over_represented_pvalue)
dim(go_results_BP_up)## [1] 654 7go_results_BP_down <- GO_05_down %>%
filter(ontology=="BP")%>%
filter(over_represented_pvalue != "NA") %>%
#filter(numInCat>10)%>%
arrange(., over_represented_pvalue)
dim(go_results_BP_down)## [1] 298 7all_enriched_terms_up_down <- c(go_results_BP_up$GOterm,go_results_BP_down$GOterm)
length(all_enriched_terms_up_down)## [1] 952length(unique(all_enriched_terms_up_down))## [1] 947By reducing the lists together, we can ensure that each GO term is given the same parent term whether the term was in the Up or Down modules.
library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package
simMatrix <- calculateSimMatrix(unique(all_enriched_terms_up_down),
orgdb="org.Ce.eg.db", #c. elegans database
ont="BP",
method="Rel")## preparing gene to GO mapping data...## preparing IC data...#calculate similarity
reducedTerms <- reduceSimMatrix(simMatrix,
scores = "size",
threshold=0.7,
orgdb="org.Ce.eg.db")## No scores provided. Falling back to term's GO sizedim(reducedTerms)## [1] 730 10#keep only the goterms from the reduced list
go_results_BP_up_reduced <- go_results_BP_up %>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results_BP_up_reduced$ParentTerm <- reducedTerms$parentTerm[match(go_results_BP_up_reduced$GOterm, reducedTerms$go)]
length(unique(go_results_BP_up_reduced$ParentTerm))## [1] 78#keep only the goterms from the reduced list
go_results_BP_down_reduced <- go_results_BP_down %>%
filter(GOterm %in% reducedTerms$go)
#add in parent terms to list of go terms
go_results_BP_down_reduced$ParentTerm <- reducedTerms$parentTerm[match(go_results_BP_down_reduced$GOterm, reducedTerms$go)]
length(unique(go_results_BP_down_reduced$ParentTerm))## [1] 43#write.csv(go_results_BP_up_reduced, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")
#write.csv(go_results_BP_down_reduced, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")The following list is a list of BP-ontology (“Biological Process”) GO terms that were significantly enriched (P < 0.05) within the modules that were upregulated for calcification. The list has been further reduced by using the package rrvgo.
#cal_up_terms <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")
cal_up_terms <- go_results_BP_up_reduced
cal_up_terms <- cal_up_terms %>% mutate(Factor = "Up")
head(cal_up_terms)## GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 GO:0050794 8.436918e-08 1.0000000 1395
## 2 GO:0065007 1.553493e-06 0.9999989 1574
## 3 GO:0050789 1.584882e-06 0.9999989 1479
## 4 GO:0048523 1.581326e-05 0.9999878 800
## 5 GO:0048518 3.043369e-05 0.9999758 950
## 6 GO:0051336 3.403563e-05 0.9999783 232
## numInCat term ontology
## 1 2785 regulation of cellular process BP
## 2 3184 biological regulation BP
## 3 2981 regulation of biological process BP
## 4 1569 negative regulation of cellular process BP
## 5 1888 positive regulation of biological process BP
## 6 415 regulation of hydrolase activity BP
## ParentTerm Factor
## 1 regulation of macromolecule metabolic process Up
## 2 regulation of macromolecule metabolic process Up
## 3 regulation of macromolecule metabolic process Up
## 4 negative regulation of biosynthetic process Up
## 5 regulation of macromolecule metabolic process Up
## 6 regulation of catalytic activity UpThe following list is a list of BP-ontology (“Biological Process”) GO terms that were significantly enriched (P < 0.05) within the modules that were downregulated for calcification. The list has been further reduced by using the package rrvgo.
#cal_down_terms <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")
cal_down_terms <- go_results_BP_down_reduced
cal_down_terms <- cal_down_terms %>% mutate(Factor = "Down")
head(cal_down_terms)## GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 GO:0002181 8.295294e-15 1 67
## 2 GO:0006614 2.463649e-13 1 48
## 3 GO:0006412 3.096109e-13 1 123
## 4 GO:0043043 1.598313e-12 1 126
## 5 GO:0006613 2.273026e-12 1 49
## 6 GO:0006518 7.543937e-12 1 146
## numInCat term ontology
## 1 90 cytoplasmic translation BP
## 2 59 SRP-dependent cotranslational protein targeting to membrane BP
## 3 219 translation BP
## 4 231 peptide biosynthetic process BP
## 5 63 cotranslational protein targeting to membrane BP
## 6 285 peptide metabolic process BP
## ParentTerm Factor
## 1 translation Down
## 2 protein transport Down
## 3 translation Down
## 4 translation Down
## 5 protein transport Down
## 6 translation DownMerge up and down-regulation of calcification GOterms
The following list is a list of BP-ontology (“Biological Process”) GO terms that were significantly enriched (P < 0.05) within the modules that were upregulated or downregulated for calcification. The list has been further reduced by using the package rrvgo.
all_terms <- rbind(cal_down_terms,cal_up_terms)
all_terms <- all_terms[,c("Factor","GOterm","over_represented_pvalue","under_represented_pvalue","numDEInCat","numInCat","term","ontology","ParentTerm")]
all_terms$GOterm<-as.factor(all_terms$GOterm)
dim(all_terms) #734 reduced terms ## [1] 734 9length(unique(all_terms$GOterm)) #this represents 730 unique terms between the two lists of reduced terms## [1] 730length(unique(all_terms$ParentTerm)) #this represents 87 unique terms between the two lists of reduced terms## [1] 87Unique terms
This is collapsing the list from 734 rows to 730, representing the 730 unique GO terms in the list above. It collapses the information in the columns “ParentTerm” and “Factor” so that for each GO term we get the parent terms associated and if this was enriched in modules upregulated for calcification, downregulated for calcification, or both.
goterms_shared <- all_terms %>%
group_by(GOterm) %>%
dplyr::summarise(
ParentTerm = paste(unique(ParentTerm), collapse = ", "),
Factor = paste(unique(Factor), collapse = ", "),
term = paste(unique(term), collapse = ", ")
)
dim(goterms_shared)## [1] 730 4goterms_shared_full <- goterms_shared %>%
left_join(dplyr::select(all_terms,GOterm, Factor, over_represented_pvalue), by = "GOterm") %>% #select 3 columns GOterm, Factor, over_represented_pvalue from all_terms and left join by GOterm, turns this dataframe from 730 rows back to the 734 in all_terms
mutate(pvalue_Up = ifelse(Factor.y == "Up", over_represented_pvalue, NA)) %>% #add a column that is the p-value for the Up factor
mutate(pvalue_Down = ifelse(Factor.y == "Down", over_represented_pvalue, NA)) %>% #add a column that is the p-value for the Down factor
dplyr::select(-c("over_represented_pvalue", "Factor.y")) %>% #remove the unique columns so that we can collapse the dataframe
group_by(GOterm, ParentTerm, Factor.x, term) %>% #group by the repeated columns for the non-unique GO terms
dplyr::summarize(pvalue_Up = na.omit(pvalue_Up)[1], #carry over the p-value for the term in the up direction, by taking the first non-NA value.
pvalue_Down = na.omit(pvalue_Down)[1]) %>% #carry over the p-value for the term in the down direction, by taking the first non-NA value.
rename(Factor.x = "Factor") #rename column ## `summarise()` has grouped output by 'GOterm', 'ParentTerm', 'Factor.x'. You can
## override using the `.groups` argument.write.csv(goterms_shared_full, "../../output/WGCNA/GO_analysis/Merged_GOterms_factor_ParentTerm.csv")plots
goterms_shared_full <- goterms_shared_full %>% group_by(ParentTerm) %>% mutate("N_in_Parent" = n()) %>% ungroup()
goterms_up <- goterms_shared_full %>% dplyr::filter(Factor=="Up")
goterms_down <- goterms_shared_full %>% dplyr::filter(Factor=="Down")
goterms_shared_only <- goterms_shared_full %>% dplyr::filter(Factor=="Down, Up")
nrow(goterms_up)## [1] 504nrow(goterms_down)## [1] 222nrow(goterms_shared_only)## [1] 4nrow(goterms_up) + nrow(goterms_down) + nrow(goterms_shared_only) == nrow(goterms_shared_full)## [1] TRUE#how many parent terms in each
length(unique(goterms_up$ParentTerm))## [1] 77length(unique(goterms_down$ParentTerm))## [1] 42length(unique(goterms_shared_only$ParentTerm))## [1] 2freq_fig_up_pval <- ggplot(goterms_shared_only, aes(x = reorder(ParentTerm, -N_in_Parent), y = pvalue_Up)) +
geom_segment(aes(x = ParentTerm, xend = ParentTerm, y = 0, yend = pvalue_Up,),linewidth=.25) +
geom_point(size=1.5, alpha=1, colour = "red") +
geom_hline(yintercept = 0, linetype = "solid", color = "grey", size = 0.5, show.legend = TRUE) +
coord_flip() +
theme_classic() +
ylim(0, 0.04) +
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=5),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
axis.text.y = element_text(vjust=0.5, size=5),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
guides(color = FALSE)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.freq_fig_down_pval <- ggplot(goterms_shared_only, aes(x = reorder(ParentTerm, -N_in_Parent), y = pvalue_Down)) +
geom_segment(aes(x = ParentTerm, xend = ParentTerm, y = 0, yend = pvalue_Down),linewidth=.25) +
geom_point(size=1.5, alpha=1, colour = "blue") +
geom_hline(yintercept = 0, linetype = "solid", color = "grey", size = 0.5, show.legend = TRUE) +
coord_flip() +
theme_classic() +
ylim(0, 0.04) +
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=5),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
axis.text.y = element_text(vjust=0.5, size=5),
axis.title.x = element_blank(),
axis.title.y = element_blank())
compare_figs<-cowplot::plot_grid(freq_fig_up_pval, freq_fig_down_pval, nrow=1, align="v")
compare_figs
ggsave(filename="../../output/WGCNA/GO_analysis/Up_Down_Shared_ParentTerms.pdf", plot=compare_figs, dpi=300, height=3, units="in", limitsize=FALSE)## Saving 7 x 3 in imagefreq_fig_up_pval <- ggplot(goterms_up, aes(x = reorder(ParentTerm, -N_in_Parent), y = pvalue_Up)) +
geom_segment(aes(x = ParentTerm, xend = ParentTerm, y = 0, yend = pvalue_Up,),linewidth=.25) +
geom_point(size=1.5, alpha=1, colour = "red") +
geom_hline(yintercept = 0, linetype = "solid", color = "grey", size = 0.5, show.legend = TRUE) +
coord_flip() +
theme_classic() +
ylim(0, 0.05) +
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=5),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
axis.text.y = element_text(vjust=0.5, size=5),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
guides(color = FALSE)
freq_fig_down_pval <- ggplot(goterms_down, aes(x = reorder(ParentTerm, -N_in_Parent), y = pvalue_Down)) +
geom_segment(aes(x = ParentTerm, xend = ParentTerm, y = 0, yend = pvalue_Down),linewidth=.25) +
geom_point(size=1.5, alpha=1, colour = "blue") +
geom_hline(yintercept = 0, linetype = "solid", color = "grey", size = 0.5, show.legend = TRUE) +
coord_flip() +
theme_classic() +
ylim(0, 0.05) +
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=5),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
axis.text.y = element_text(vjust=0.5, size=5),
axis.title.x = element_blank(),
axis.title.y = element_blank())
compare_figs<-cowplot::plot_grid(freq_fig_up_pval, freq_fig_down_pval, nrow=1, align="v")
compare_figs
ggsave(filename="../../output/WGCNA/GO_analysis/Up_Down_Unique_ParentTerms.pdf", plot=compare_figs, dpi=300, height=10, units="in", limitsize=FALSE)## Saving 7 x 10 in imageGOSlim
I downloaded the GOSlim terms list from the following website on 4/5/2024: http://www.informatics.jax.org/gotools/data/input/map2MGIslim.txt
I did so by going to this link and right clicking “here” at “Tab-delimited file mapping GO_ids to MGI GO_Slim category available for download here” and saying “Save link as…” , and I saved it as “map2MGIslim.txt”. I then converted this to CSV and uploaded to this github repository here.
Add slim terms to dataset
GOslim <- read.csv("../../data/map2MGIslim.csv") %>% dplyr::select(-c(term,aspect))goterms_shared_full_slim <- goterms_shared_full %>%
inner_join(GOslim, by = c("GOterm" = "GO_id"))
goterms_up_slim <- goterms_shared_full_slim %>% dplyr::filter(Factor=="Up" | Factor=="Down, Up")
goterms_up_full_slim_plot <- goterms_up_slim %>%
ggplot(aes(x=pvalue_Up,
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Over-represented p-value", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text.y = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
); goterms_up_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Up_GOSlim.pdf", goterms_up_full_slim_plot, width = 5, height = 35)
goterms_up_full_slim_plot <- goterms_up_slim %>%
ggplot(aes(x=log10(pvalue_Up),
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Log 10(Over-represented p-value)", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text.y = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
); goterms_up_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Up_GOSlim_Log.pdf", goterms_up_full_slim_plot, width = 5, height = 35)goterms_down_slim <- goterms_shared_full_slim %>% dplyr::filter(Factor=="Down" | Factor=="Down, Up")
goterms_down_full_slim_plot <- goterms_down_slim %>%
ggplot(aes(x=pvalue_Down,
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Over-represented p-value", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
legend.text=element_text(size = 5),
legend.title = element_text(size = 5)
); goterms_down_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Down_GOSlim.pdf", goterms_down_full_slim_plot, width = 5, height = 35)
goterms_down_full_slim_plot <- goterms_down_slim %>%
ggplot(aes(x=log10(pvalue_Down),
y=term)) +
geom_point(size = 0.5) +
expand_limits(x=0) +
facet_grid(GOSlim_bin ~ ., scales = "free", space='free', labeller = label_wrap_gen(width = 55, multi_line = TRUE)) +
labs(x="Log 10(Over-represented p-value)", y="") +
theme_bw() +
theme(
strip.text.y = element_text(size = 5, colour = "black", angle = 0, face = "bold"),
axis.text = element_text(size = 1, colour = "black"),
axis.title.x = element_text(size = 5, colour = "black"),
legend.text=element_text(size = 5),
legend.title = element_text(size = 5)
); goterms_down_full_slim_plot
ggsave("../../output/WGCNA/GO_analysis/Calc_Down_GOSlim_Log.pdf", goterms_down_full_slim_plot, width = 5, height = 35)For each parent term in this list, how many are “Up” by calcification, “Down”, or both
SharedGOterms = # of GO terms within the parent term that are in the list for “Down”, “Up”, or “Down, Up”
result_parent_unique <- goterms_shared %>%
group_by(ParentTerm,Factor) %>%
dplyr::summarise(SharedGOterms = n_distinct(GOterm)) %>%
arrange(-SharedGOterms)## `summarise()` has grouped output by 'ParentTerm'. You can override using the
## `.groups` argument.parent_filtered_up <- result_parent_unique %>%
dplyr::filter(Factor=="Up" | Factor=="Down, Up")
#dplyr::filter(SharedGOterms>=5) parent_filtered_down <- result_parent_unique %>%
dplyr::filter(Factor=="Down" | Factor=="Down, Up")
#dplyr::filter(SharedGOterms>=5) Count number of GOterms by ParentTerm for the upregulation of calcification
result_up <- cal_up_terms %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Number.of.terms = n_distinct(term))%>%
mutate(Calcification.direction = "Up")
head(result_up)## # A tibble: 6 × 3
## ParentTerm Number.of.terms Calcification.direct…¹
## <chr> <int> <chr>
## 1 ATP biosynthetic process 5 Up
## 2 Arp2/3 complex-mediated actin nucleati… 16 Up
## 3 DNA dealkylation involved in DNA repair 6 Up
## 4 Golgi organization 2 Up
## 5 IRE1-mediated unfolded protein response 16 Up
## 6 P granule assembly 1 Up
## # ℹ abbreviated name: ¹Calcification.directiondim(result_up)## [1] 78 3merged_up <- parent_filtered_up %>%
tidyr::separate_rows(ParentTerm, sep = ", ") %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Sum_of_SharedGOterms = sum(SharedGOterms, na.rm = TRUE)) %>%
left_join(result_up, by = "ParentTerm") #join with result from above to get the Number.of.Terms column
merged_up_clean <- na.omit(merged_up)
head(merged_up_clean)## # A tibble: 6 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 ATP biosynthetic … 5 5 Up
## 2 Arp2/3 complex-me… 16 16 Up
## 3 DNA dealkylation … 6 6 Up
## 4 Golgi organization 2 2 Up
## 5 IRE1-mediated unf… 16 16 Up
## 6 P granule assembly 1 1 Up
## # ℹ abbreviated name: ¹Calcification.directiondim(merged_up_clean)## [1] 78 4Count number of GOterms by ParentTerm for the downregulation of calcification
result_down <- cal_down_terms %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Number.of.terms = n_distinct(term))%>%
mutate(Calcification.direction = "Down")
dim(result_down)## [1] 43 3head(result_down)## # A tibble: 6 × 3
## ParentTerm Number.of.terms Calcification.direct…¹
## <chr> <int> <chr>
## 1 ATP biosynthetic process 24 Down
## 2 Arp2/3 complex-mediated actin nucleati… 2 Down
## 3 IRE1-mediated unfolded protein response 2 Down
## 4 NADH regeneration 1 Down
## 5 P granule assembly 1 Down
## 6 biological process involved in intersp… 1 Down
## # ℹ abbreviated name: ¹Calcification.directionmerged_down <- parent_filtered_down %>%
tidyr::separate_rows(ParentTerm, sep = ", ") %>%
dplyr::group_by(ParentTerm) %>%
dplyr::summarize(Sum_of_SharedGOterms = sum(SharedGOterms, na.rm = TRUE)) %>%
left_join(result_down, by = "ParentTerm")
merged_down_clean<- na.omit(merged_down)
head(merged_down_clean)## # A tibble: 6 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 ATP biosynthetic … 24 24 Down
## 2 Arp2/3 complex-me… 2 2 Down
## 3 IRE1-mediated unf… 2 2 Down
## 4 NADH regeneration 1 1 Down
## 5 P granule assembly 1 1 Down
## 6 biological proces… 1 1 Down
## # ℹ abbreviated name: ¹Calcification.directionFrequency >10 up
cal_freq_terms_filtered_up <- merged_up_clean %>%
filter(Number.of.terms>=10) %>%
filter(Calcification.direction=="Up")
dim(cal_freq_terms_filtered_up)## [1] 18 4Figure
#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), group=1))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,30))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_up
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up.pdf", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageFrequency >10 down
cal_freq_terms_filtered_down <- merged_down_clean %>%
filter(Number.of.terms>=10)
#filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down## # A tibble: 7 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 ATP biosynthetic … 24 24 Down
## 2 biosynthetic proc… 15 15 Down
## 3 metabolic process 13 13 Down
## 4 organic acid meta… 12 12 Down
## 5 peptidyl-serine p… 13 13 Down
## 6 proteasome-mediat… 24 24 Down
## 7 protein transport 13 13 Down
## # ℹ abbreviated name: ¹Calcification.directionFigure
freq_fig_down<-ggplot(cal_freq_terms_filtered_down, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms)))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
#geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,30))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+
#scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_down
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down.pdf", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imagecompare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, nrow=2, align="v")
compare_figs
Frequency >20 up
cal_freq_terms_filtered_up <- merged_up_clean %>%
filter(Number.of.terms>=20) %>%
filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up## # A tibble: 4 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 positive regulati… 24 24 Up
## 2 regulation of cal… 22 22 Up
## 3 regulation of cat… 21 21 Up
## 4 regulation of mac… 20 20 Up
## # ℹ abbreviated name: ¹Calcification.directionFigure
#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms),group=1))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,30))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_up
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_gr20.pdf", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imageFrequency >20 down
cal_freq_terms_filtered_down <- merged_down_clean %>%
filter(Number.of.terms>=20)
#filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down## # A tibble: 2 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 ATP biosynthetic … 24 24 Down
## 2 proteasome-mediat… 24 24 Down
## # ℹ abbreviated name: ¹Calcification.directionFigure
freq_fig_down<-ggplot(cal_freq_terms_filtered_down, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms)))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
#geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,30))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+
#scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_down
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_gr20.pdf", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)## Saving 7 x 12 in imagecompare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, nrow=2, align="v")
compare_figs
All terms up
cal_freq_terms_filtered_up_all <- merged_up_clean %>%
filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up_all## # A tibble: 78 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 ATP biosynthetic… 5 5 Up
## 2 Arp2/3 complex-m… 16 16 Up
## 3 DNA dealkylation… 6 6 Up
## 4 Golgi organizati… 2 2 Up
## 5 IRE1-mediated un… 16 16 Up
## 6 P granule assemb… 1 1 Up
## 7 abscission 1 1 Up
## 8 biological proce… 3 3 Up
## 9 cell communicati… 4 4 Up
## 10 cell differentia… 8 8 Up
## # ℹ 68 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up_all, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms),group=1))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,30))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_up
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_ALL.pdf", plot=freq_fig_up, dpi=300, height=17, units="in", limitsize=FALSE)## Saving 7 x 17 in imageAll terms down
cal_freq_terms_filtered_down_all <- merged_down_clean %>%
filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down_all## # A tibble: 42 × 4
## ParentTerm Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
## <chr> <int> <int> <chr>
## 1 ATP biosynthetic… 24 24 Down
## 2 Arp2/3 complex-m… 2 2 Down
## 3 IRE1-mediated un… 2 2 Down
## 4 NADH regeneration 1 1 Down
## 5 P granule assemb… 1 1 Down
## 6 biological proce… 1 1 Down
## 7 biosynthetic pro… 15 15 Down
## 8 cell communicati… 3 3 Down
## 9 cellular aldehyd… 1 1 Down
## 10 cellular macromo… 2 2 Down
## # ℹ 32 more rows
## # ℹ abbreviated name: ¹Calcification.directionFigure
freq_fig_down<-ggplot(cal_freq_terms_filtered_down_all, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms)))+
#facet_wrap(~Calcification.direction, nrow = 1)+
geom_point(size=5, alpha=1, pch=21,color="black")+
geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
#geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
coord_flip()+
scale_y_continuous(expression(GO~term~counts),limits=c(0,30))+
#scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
#scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+
#scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+
#scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+
theme_classic()+
theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
panel.background= element_rect(fill=NA, color='black'),
legend.title = element_blank(),
axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger
axis.title.x = element_blank(),#making the axis title larger
axis.title.y = element_blank(),
strip.text = element_text(size=12))#making the axis title larger
freq_fig_down
ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_ALL.pdf", plot=freq_fig_down, dpi=300, height=17, units="in", limitsize=FALSE)## Saving 7 x 17 in imagecompare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, ncol=2, align="h")
compare_figs